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Essentials of Probability and Statistical Inference III: Theory of Modern Statistical Methods

Course Status

3rd Term
Academic Year
2023 - 2024
Instruction Method
Synchronous Online
Class Time(s)
M, W, 10:30 - 11:50am
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year

Working knowledge of calculus

Builds on the concepts discussed in 140.646 and 140.647 to lay out the foundation for both classical and modern theory/methods for drawing statistical inference. Includes classical unbiased estimation, unbiased estimating equations, likelihood and conditional likelihood inference, linear models and generalized linear models, and other extended topics. De-emphasizes mathematical proofs and replaces them with extended discussion of interpretation of results and examples for illustration.
Learning Objectives
Upon successfully completing this course, students will be able to:
  1. Identify the likelihood principal and become aware the multiple ways to achieve an estimator that satisfies the likelihood principal. For classic parametric models, be able to derive the maximum likelihood estimator.
  2. Comprehend the properties of maximum likelihood estimators for parametric models and use it for statistical inference.
  3. Deeply identify the philosophy behind statistical inference within the frequentist framework. Be able to conduct hypothesis testing for some specific context.
Methods of Assessment
This course is evaluated as follows:
  • 50% 4-5 problem sets
  • 50% Final Exam
Special Comments

Please note: This is the virtual/online section of a course that is also offered onsite. Students will need to commit to the modality for which they register. One 1-hour lab per week (time TBA)